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Open Access Highly Accessed Research

Identification of new biomarker candidates for glucocorticoid induced insulin resistance using literature mining

Wilco WM Fleuren12, Erik JM Toonen3, Stefan Verhoeven4, Raoul Frijters16, Tim Hulsen17, Ton Rullmann5, René van Schaik4, Jacob de Vlieg14 and Wynand Alkema18*

Author Affiliations

1 Computational Drug Discovery (CDD), CMBI, NCMLS, Radboud University Nijmegen Medical Centre, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands

2 Netherlands Bioinformatics Centre (NBIC), P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands

3 Department of Medicine, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands

4 Netherlands eScience Center, Amsterdam, The Netherlands

5 TNO, Zeist, The Netherlands

6 Present address: Rijk Zwaan Nederland BV, Fijnaart, The Netherlands

7 Present address: Philips Research Europe, Eindhoven, The Netherlands

8 Present address: NIZO Food Research BV, Ede, The Netherlands

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BioData Mining 2013, 6:2  doi:10.1186/1756-0381-6-2

Published: 4 February 2013

Abstract

Background

Glucocorticoids are potent anti-inflammatory agents used for the treatment of diseases such as rheumatoid arthritis, asthma, inflammatory bowel disease and psoriasis. Unfortunately, usage is limited because of metabolic side-effects, e.g. insulin resistance, glucose intolerance and diabetes. To gain more insight into the mechanisms behind glucocorticoid induced insulin resistance, it is important to understand which genes play a role in the development of insulin resistance and which genes are affected by glucocorticoids.

Medline abstracts contain many studies about insulin resistance and the molecular effects of glucocorticoids and thus are a good resource to study these effects.

Results

We developed CoPubGene a method to automatically identify gene-disease associations in Medline abstracts. We used this method to create a literature network of genes related to insulin resistance and to evaluate the importance of the genes in this network for glucocorticoid induced metabolic side effects and anti-inflammatory processes.

With this approach we found several genes that already are considered markers of GC induced IR, such as phosphoenolpyruvate carboxykinase (PCK) and glucose-6-phosphatase, catalytic subunit (G6PC). In addition, we found genes involved in steroid synthesis that have not yet been recognized as mediators of GC induced IR.

Conclusions

With this approach we are able to construct a robust informative literature network of insulin resistance related genes that gave new insights to better understand the mechanisms behind GC induced IR. The method has been set up in a generic way so it can be applied to a wide variety of disease networks.

Keywords:
Literature mining; Insulin resistance; Glucocorticoids; Gene networks